# Quiz study guides

You will receive a single score (0-4, see rubric) for each topic area representing your understanding of the course material in that area. A great way to study for quizzes in general is to (1) study the lecture notes and (2) quiz yourself with the labs.

## 1 Quiz 1

Quiz 1 will test the following learning objectives, divided into 6 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

**R Basics: general**- Assign an object to a valid variable name, list all variables in the environment and remove them
- Use packages and differentiate between installing and loading
- Get help with a function or package from R
- Return information about an object, including its structure, data type, length, and attributes
- Explain what functions and control flow are; differentiate between types of control flow

**R Basics: vectors, operations, and subsetting**- Distinguish between an atomic vector and a list
- Create atomic vectors and determine their data types
- Differentiate between implicit and explicit coercion and coerce an object to another type
- Use arithmetic, comparison, and logical operators on vectors
- Explain how more complex data structures are built from atomic vectors and create them
- Distinguish between
`NA`

and`NULL`

- Subset vectors and higher dimensional objects with the
`[`

,`[[`

and`$`

operators

**Data importing**- Load the
`tidyverse`

, recognize the included packages, and critique code for redundant loading - Construct a tidy dataset and critique whether a given dataset is tidy
- Use the map function from the
`purr`

package - Create a tibble and distinguish between a tibble and a data frame
- Use
`readr`

to read delimited files and determine whether`readr`

can read files of a given type - Use
`col_types`

to add a column specifications and explain how readr guesses without it - Solve the 3 most common importing problems we discussed in class

- Load the
**Data visualization: basics**- Describe how to create a plot with
`ggplot2`

including the 3 basic requirements - Distinguish between mapping and setting aesthetics
- Describe how
`ggplot2`

maps categorical variables to aesthetics and interpret the 3 common warnings people encounter in this process - Interpret
`ggplot()`

calls with explicit or implicit arguments for data and mapping - Recognize the geoms we discussed in class and select which to use for a given situation
- Differentiate between globally and locally defined mappings and recognize them in given plot (or code)

- Describe how to create a plot with
**Data visualization: layers**- Use the
`position`

argument to modify the position of the geoms in`geom_bar()`

or`geom_point()`

- Describe
`stat="identity"`

and describe the default transformations for`geom_bar()`

,`geom_histogram()`

, and`geom_smooth()`

- Set the smoothing method for
`geom_smooth()`

and the bins or bindwidth for`geom_histogram()`

- Facet a plot with
`facet_wrap()`

and`facet_grid()`

- Modify axis, legend, and plot labels with
`labs()`

- Apply a given theme to a plot and adjust the base font size or family.
- Describe scales and recognize the outcome of adding a scale layer

- Use the
**Data wrangling**- Describe the common structure of
`dplyr`

functions (aka verbs) - Combine
`dplyr`

functions with the pipe operator to solve complex problems - Manipulate rows with
`filter()`

,`arrange()`

, and`distinct()`

- Maniuplate columns with
`mutate()`

,`select()`

, and`rename()`

- Group and summarise data with
`group_by()`

,`summarise()`

, and`ungroup()`

- Evaulate
`dplyr`

functions that include the common arguments we covered in class

- Describe the common structure of

## 2 Quiz 2

Quiz 2 will test the following learning objectives, divided into 3 topic areas. For each topic area, you should be able to do the list that follows. You can think of this as a studying checklist!

**Sampling distribution**- Explore a dataset with an appropriate figure (histogram, boxplot, scatterplot) and summary statistics appropriate for the distribution.
- Recognize uniform and Gaussian probability distributions in a plot or equation and use R’s functions
`d*()`

,`p*()`

, and`r*()`

to work with these distributions - Explain the difference between the parameter and the paramter estimate
- Construct the sampling distribution of a paramater estimate with
`infer`

and quantify the spread of the distribution with a confidence interval.

**Hypothesis testing**- Given a set of data, implement the 3-step hypothesis testing framework
**nonparametrically**: (1) Pose a null hypothesis, (2) quantify how likely a given pattern of results is under the null, and (3) determine whether to reject the null (conceptually and with the`infer`

framework). - Given a theoretical distriubiton (e.g. t), implement the 3-step hypothesis testing framework
**parametrically**. - Given an observed correlation, determine whether a correlation is positive, negative, or no correlation.
- Explain correlation as model building

- Given a set of data, implement the 3-step hypothesis testing framework
**Model specification**- Classify a model as supervised or unsupervised, regression or classification, and linear or nonlinear
- Identify the response and explanatory variables from a given research question or model
- Recognize the 4 ways of writing the linear model equation
- Select the equation (e.g \(y = \beta_0 + \beta_1x_1 + \beta_2x_2\)) or R expression (e.g.
`y ~ year + gender`

) for a plotted model